1. Field of the Invention
The invention relates to a method and apparatus for the determination and tracking of multiple objects.
2. Brief Description of the Related Art
The acronym MOT used in this application stands for Multiple Object Tracking. The acronym PDF used in this application stands for Probability Density Function. The acronym ADAS in this application stands for Advanced Driver Assistance Systems.
M. Munz, K. Dietmayer, and M. Mählisch, “A Sensor Independent Probabilistic Fusion System for Driver Assistance Systems,” in Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems (ITSC 2009), St. Louis, Mo., U.S.A, October 2009 teach a so-called probabilistic fusion framework for implementing a sensor independent measurement fusion. The probabilistic fusion framework enables exchange of sensors (101) used in a multiple object tracking system for the tracking of objects. A sensor of type A can be replaced by a sensor of type B without changing the measurement fusion itself. The interfaces used in this paper described probabilistic descriptions of measurement and existence uncertainties. The work of Munz at al aims at defining one generic interface between a sensor model and the multiple object trackers. The modules for state estimation of the tracked objects, existence estimation of the tracked objects, and associations of data delivered by the sensor with tracked objects, of the multiple object tracker are not modularized. The general interface described in the work of Munz et al is restricted to a Gaussian representation of the spatial measurement distribution and a binary cardinality distribution for both the clutter and the measurements. Munz et al do not describe an arbitrary PDF for the spatial distribution of sensor measurements as well as arbitrary probability distributions for the cardinality models.
International Patent Application No. WO2004111938 teaches a method for object recognition in a driver aid system for motor vehicles. The method of the '938 discloses fusion of data of different sensors to obtain a Maximum A-Posteriori (MAP) estimate. The MAP estimate is a point estimate of a probability density function. In case of Gaussian distribution, the MAP estimate coincides with the mean of the Gaussian. The method of the '938 disclosure implements one type of the MOT and statically defines all of the interfaces between processing blocks. It does not facilitate any exchange of the processing blocks.
Similarly, the European patent application EP1634241 also describes a MOT for the fusion of radar data and camera data. The interfaces between the processing blocks taught in the '241 application are not of generic nature.
German Patent Application DE102006045115 claims a generic MOT using only Gaussian functions to describe the PDF.
U.S. Pat. No. 7,460,951 claims a MOT with a system model. This US patent fails also to disclose a generic interface structure of the disclosed system. U.S. Pat. No. 7,747,084 claims a structure for the MOT but fails to teach verification of the configuration of the MOT during the MOT's development.
A publication by Eric Richter—Non-Parametric Bayesian Filtering for Multiple Object Tracking (ISBN: 978-3-8440-1488-4) teaches case studies that describe different implementations of MOTs. The first of these case studies proposes an implementation based on data fusion between data from radar and camera sensors. The teachings of this publication fail to describe neither generic interfaces nor the proposed modularization.
Schubert—Integrated Bayesian Object and Situation Assessment for Lane Change Assistance (ISBN: 978-3-8440-0322-2) proposes interfaces between MOTs and Bayesian networks that are used for assessing the relationships between different objects. These interfaces can be used to pass the results of an MOT to a subsequent system that takes automatic decisions based on the current situation or utilize situation information for influencing the object tracking modules. However, the publication does not contain any interfaces within an MOT nor the proposed modularization.
A further discussion is to be found in R. Schubert, C. Adam, E. Richter, S. Bauer, H. Lietz, and G. Wanielik, “Generalized probabilistic data association for vehicle tracking under clutter”, in Intelligent Vehicles Symposium (IV), 2012 IEEE, Alcala de Henares, Spain, June 2012